Jef Caers

Professor of Geological Sciences

Bio

Jef Caers received both an MSc (’93) in mining engineering / geophysics and a PhD (’97) in engineering from the Katholieke Universiteit Leuven, Belgium. Currently, he is Professor of Geological Sciences (since 2015) and previously Professor of Energy Resources Engineering at Stanford University, California, USA. He is also director of the Stanford Center for Reservoir Forecasting, an industrial affiliates program in reservoir modeling and geostatistics with ~20 partners from the Energy Industry. Dr. Caers’ research interests are in the area of geostatistics, spatial modeling and modeling uncertainty applied to various areas in the Earth Sciences. He was awarded the Vistelius award by the IAMG in 2001, is Editor-in-Chief of Computers and Geosciences and served as chairman for the IAMG 2009 conference. Dr. Caers has received several best paper awards and written three books entitled "Petroleum Geostatistics” (SPE) “Modeling Uncertainty in the Earth Sciences” (Wiley-Blackwell) and "Multiple-point Geostatistics: stochastic modeling with training images" is published with Wiley-Blackwell in 2014. Dr. Caers was awarded the 2014 Krumbein Medal of the IAMG for career achievement.

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Current Research and Scholarly Interests

ResearchMy research occurs at the intersection of statistical science, computer science and the geological sciences. What is the fundamental research question I want to address? I believe that from a data-scientific point of view, most geological data and modeling questions can be broadly classified as problems that are high-dimensional but have small sample size. Data are often sparse and computer experiments we run are CPU demanding, resulting in some low sample size. Yet the understanding we attempt to develop requires complex physical or geochemical models, analysis of multivariate, spatial problems over potentially large areas, require aggregation of data at various scale (in space and time) and hence are high dimensional problems. How do we formulate such problems? What are fundamental mathematical and computer science methods for analyzing such problems? How can we build predictive models for such problems? How do we integrate the various disciplines involved? Most of machine learning and statistics research currently does not take place in this setting.

In terms of machine learning, I do not prescribe to the narrow set of tools it usually encapsulates (e.g. kernel learning), but look at the wider use of the “machine” to study our type of problems, including modern fields such as computer graphics and computer vision. In understanding, modeling and forecasting in complex geological systems I believe there is a need for general methods for 1) quantifying sensitivities in such systems and their various interactions 2) learning with data acquired in the field that can be diverse in nature and different in scales of observation and 3) quantifying our lack of understanding through probabilistic models, which is essential for risk quantification & decision making.

Specific areas I am interested in:

Oil/Gas. The subsurface characterization, both depositional & structural of reservoir systems from seismic, well and production data for forecasting requires an integrated set of approaches involving both physical and statistical modeling. I work on comprehensive approaches to forecasting recovery of fluids from the subsurface.

Groundwater/hydrology. The characterization of groundwater systems evidently has many analogies with petroleum reservoirs. My research will focuses on the aquifer to basin scale. I am interested in saltwater intrusion problems, quantitative characterization of karst systems and predicting with reactive transport models.

Minerals. The exploration/ exploitation of minerals deposits will be increasing in importance considering the increasing importance of battery technologies. This would also mean that there will be an increased interest in developing geostatistical methods for the purpose of mineral potential mapping as well as ore body evaluation (economic geology). I am interested in the multi-variate & compositional nature of this problem (geochemistry) as well as scaling issues.

Modeling Spatial and Structural Uncertainty in the Subsurface Computational Challenges in the Geosciences Institute for Mathematics and its Applications, The IMA Volumes in Mathematics and its ApplicationsGerritsen, M., Caers, J.2013; 156: 143-167

History matching by jointly perturbing local facies proportions and their spatial distribution: Application to a North Sea reservoirJOURNAL OF PETROLEUM SCIENCE AND ENGINEERINGHoffman, B. T., Caers, J.2007; 57 (3-4): 257-272

A geostatistical approach to integrating data from multiple and diverse sources: An application to the integration of well data, geological information, 3d/4d geophysical and reservoir-dynamics data in a north-sea reservoirSubsurface Hydrology: Data Integration for Properties and ProcessesCaers, J., Castro, S.2007; 171: 61-71

A new multiple-grid method for multiple-scale stochastic simulation with patterns2005 Annual conference of the International Association for Mathematical GeologyHongmei, L., Arpat, B. G., Caers, J.2005

A numerical maximum likelihood method for estimating the mean of a compound lognormal distribution26th International Symposium on the Application of Computers and Operations Research in the Mineral Industry (APCOM)Caers, J., Vervoort, A.SOC MIN ENGINEERS AIME.1996: 27–32